Feasibility of ultra-low-dose noncontrast CT based on deep learning image reconstruction to evaluate chest lesions

doi:10.3969/j.issn.1674-8115.2022.08.011

Abstract

Abstract:

Objective ·To explore the feasibility of using noncontrast ultra-low-dose CT (ULDCT) to evaluate chest target lesions based on response evaluation criteria in solid tumors (RECIST) and ground glass nodules (GGNs). Methods ·From April to June 2020, patients who underwent noncontrast chest ULDCT (0.07?0.14 mSv) and low-dose enhanced CT (2.38 mSv) and had measurable target lesions defined by RECIST and GGNs ≤1 cm in diameter were included. Four sets of CT images were reconstructed for each patient, including 3 sets of ULDCT images, i.e., adaptive statistical iterative reconstruction-V with an 80% strength level (ASIR-V-80%), deep learning image reconstruction of moderate strength (DLIR-M) and deep learning image reconstruction of high strength (DLIR-H), and one set of enhanced CT images as the reference. Results ·Eighty patients who had 80 target lesions and 27 GGNs met the inclusion criteria, and the average age was (62±11) years old. Between the ULDCT images (3 sets of image reconstruction) and enhanced CT, the measured values of target lesions (r=0.988, 0.987 and 0.990, respectively), GGNs≤1 cm in diameter (r=0.905, 0.906 and 0.969, respectively), mediastinal lymph node target lesions (r=0.969, 0.957 and 0.977, respectively), and hilar lymph node target lesions (r=0.972, 0.994 and 0.994, respectively) were highly correlated. Bland-Altman analysis showed that the difference between the measured size of lung target lesion and reference value in DLIR-H reconstruction image was 4.3% (95% limits of agreement: -5.7%?14.3%), and the difference between the measured size of mediastinal lymph node target lesion and reference value was 5.1% (-9.1%?19.3%), which was better than that of ASIR-V-80% [8.5% (-3.3%?20.3%), 9.7% (-6.0%?25.3%)] and DLIR-M [8.5% (-4.2%?21.3%), 8.8% (-9.9%?27.5%)]. The difference between the measured size of lymph node target lesions in DLIR-H images and the reference value was 18.3% (8.8%?27.9%), better than ASIR-V-80% [20.2% (-1.2%?41.5%)] and DLIR-M [23.4% (13.5%?33.2%)]. The difference between the measured size of GGNs in DLIR-H images and the reference value was 7.0% (-5.7%?19.7%), better than ASIR-V-80% [14.4% (-4.4%?33.2%)] and DLIR-M [16.3% (-4.1%?36.7%)]. Conclusion ·Noncontrast ULDCT based on DLIR-H is highly correlated and consistent with the traditional enhanced CT in evaluating chest target lesions and GGNs (≤1 cm in diameter), which is conducive to repeated scanning of tumors and GGNs at a significantly reduced radiation dose.

Key words: lung cancer, RECIST, deep learning, ultra-low-dose CT

CLC Number:

R445.4

ZHAO Keke, JIANG Beibei, ZHANG Lu, WANG Lingyun, ZHANG Yaping, XIE Xueqian. Feasibility of ultra-low-dose noncontrast CT based on deep learning image reconstruction to evaluate chest lesions[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2022, 42(8): 1062-1069.

Figures/Tables 7

TrendMD

References 18

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Variable	Included patient		P value
Variable	0.07 mSv （n=40）	0.14 mSv （n=40）	P value
Age/year	64±9	61±12	0.165
Gender/n （%）
Male	28 （52）	26 （48）	0.633
Female	12 （46）	14 （54）
BMI/（kg·m^-2）	23.14±3.61	22.30±3.03	0.173
<18.5	3	4	0.243
≥18.5 and <25.0	26	31
≥25.0	11	5
Lung target tumor lesion/n
Malignant	13	15	0.377
Benign or no histological result	9	17
Mediastinal lymph node/n
Malignant	8	3	0.793
Benign or no histological result	4	2
Hilar lymph node/n
Malignant	2	3	0.294
Benign or no histological result	3	1

Variable	Included patient		P value
Variable	0.07 mSv （n=40）	0.14 mSv （n=40）	P value
Age/year	64±9	61±12	0.165
Gender/n （%）
Male	28 （52）	26 （48）	0.633
Female	12 （46）	14 （54）
BMI/（kg·m^-2）	23.14±3.61	22.30±3.03	0.173
<18.5	3	4	0.243
≥18.5 and <25.0	26	31
≥25.0	11	5
Lung target tumor lesion/n
Malignant	13	15	0.377
Benign or no histological result	9	17
Mediastinal lymph node/n
Malignant	8	3	0.793
Benign or no histological result	4	2
Hilar lymph node/n
Malignant	2	3	0.294
Benign or no histological result	3	1

Item	r
Item	ASIR-V-80% vs enhanced CT	DLIR-M vs enhanced CT	DLIR-H vs enhanced CT
All lung target tumor lesion	0.988	0.987	0.990
Malignant	0.985	0.980	0.986
Benign or no histological result	0.991	0.993	0.994
GGN （diameter≤1 cm）	0.905	0.906	0.969
Mediastinal lymph node	0.969	0.957	0.977
Malignant	0.952	0.930	0.955
Benign or no histological result	0.999	0.997	1.000
Hilar lymph node	0.972	0.994	0.994

Item	r
Item	ASIR-V-80% vs enhanced CT	DLIR-M vs enhanced CT	DLIR-H vs enhanced CT
All lung target tumor lesion	0.988	0.987	0.990
Malignant	0.985	0.980	0.986
Benign or no histological result	0.991	0.993	0.994
GGN （diameter≤1 cm）	0.905	0.906	0.969
Mediastinal lymph node	0.969	0.957	0.977
Malignant	0.952	0.930	0.955
Benign or no histological result	0.999	0.997	1.000
Hilar lymph node	0.972	0.994	0.994

Item	Arithmetic mean （95% limits of agreement）
Item	ASIR-V-80% vs enhanced CT	DLIR-M vs enhanced CT	DLIR-H vs enhanced CT
All lung target tumor lesion	8.5% （-3.3%‒20.3%）	8.5% （-4.2%‒21.3%）	4.3% （-5.7%‒14.3%）
Malignant	8.7% （-2.4%‒19.7%）	10.3% （-3.7%‒24.3%）	4.8% （-5.5%‒15.2%）
Benign or no histological result	8.3% （-4.4%‒21.0%）	6.7% （-3.6%‒17.0%）	3.8% （-5.9%‒13.5%）
GGN （diameter≤1 cm）	14.4% （-4.4%‒33.2%）	16.3% （-4.1%‒36.7%）	7.0% （-5.7%‒19.7%）
Mediastinal lymph node	9.7% （-6.0%‒25.3%）	8.8% （-9.9%‒27.5%）	5.1% （-9.1%‒19.3%）
Malignant	11.8% （-6.1%‒29.7%）	10.5% （-11.3%‒32.4%）	6.3% （-10.9%‒23.6%）
Benign or no histological result	5.8% （-0.4%‒11.9%）	5.7% （-3.9%‒15.2%）	2.8% （-0.2%‒5.8%）
Hilar lymph node	20.2% （-1.2%‒41.5%）	23.4% （13.5%‒33.2%）	18.3% （8.8%‒27.9%）